{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "L6DTkAvyY7ke"
   },
   "source": [
    "命名实体识别(NER)是根据类别对标记进行分类的任务，例如将标记标识为个人、组织或位置。命名实体识别数据集的一个例子是CoNLL-2003数据集，它完全基于该任务。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 721
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 13428,
     "status": "ok",
     "timestamp": 1586678266719,
     "user": {
      "displayName": "Xue Jiang",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GjfXAo9TmWFYS-GhDq8HOVk3N1FnJiyBPH8h9M=s64",
      "userId": "12544639825510830792"
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    "id": "2DxXpdP1Y9iO",
    "outputId": "11f66324-e62f-496e-f84c-d81fb3723bc0"
   },
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "!pip install transformers\n",
    "import torch"
   ]
  },
  {
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   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Tesla P4'"
      ]
     },
     "execution_count": 2,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.get_device_name(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7h2wzdo7ZVK0"
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   "source": [
    "### 下面是一个使用模型和Tokenizer进行命名实体识别的示例。该过程如下：\n",
    "\n",
    "– 从checkpoint名称实例化一个tokenizer和一个模型。该模型被识别为一个BERT模型，并用存储在checkpoint中的权重加载它。\n",
    "\n",
    "– 定义用于训练模型的标签列表。\n",
    "\n",
    "– 定义一个包含已知实体的序列，例如“Hugging Face”作为一个组织，“New York City”作为一个位置。\n",
    "\n",
    "– 将单词拆分为标记，以便它们可以映射到预测。使用一个小技巧，首先对序列进行完全的编码和解码，这样就留下了一个包含特殊标记的字符串。\n",
    "\n",
    "– 将该序列编码为id(自动添加特殊标记)。\n",
    "\n",
    "– 通过将输入传递到模型并获得第一个输出来检索预测。这将导致每个标记在9个可能的类上分布。我们使用argmax来检索每个标记最可能的类。\n",
    "\n",
    "– 将每个标记及其预测到一起并打印出来。"
   ]
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      "\n"
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      "\n"
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     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModelForTokenClassification, AutoTokenizer\n",
    "\n",
    "model = AutoModelForTokenClassification.from_pretrained(\"dbmdz/bert-large-cased-finetuned-conll03-english\")\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n",
    "\n",
    "label_list = [\n",
    "    \"O\",       # 不是命名实体\n",
    "    \"B-MISC\",  # 一个杂项实体的开头\n",
    "    \"I-MISC\",  # 杂项\n",
    "    \"B-PER\",   # 一个人名的开头\n",
    "    \"I-PER\",   # 人名\n",
    "    \"B-ORG\",   # 一个组织的开头\n",
    "    \"I-ORG\",   # 组织\n",
    "    \"B-LOC\",   # 一个地点的开头\n",
    "    \"I-LOC\"    # 地点\n",
    "]\n",
    "\n",
    "sequence = \"Hugging Face Inc. is a company based in New York City. Its headquarters are in DUMBO, therefore very\" \\\n",
    "           \"close to the Manhattan Bridge.\"\n"
   ]
  },
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     "text": [
      "[('[CLS]', 'O'), ('Hu', 'I-ORG'), ('##gging', 'I-ORG'), ('Face', 'I-ORG'), ('Inc', 'I-ORG'), ('.', 'O'), ('is', 'O'), ('a', 'O'), ('company', 'O'), ('based', 'O'), ('in', 'O'), ('New', 'I-LOC'), ('York', 'I-LOC'), ('City', 'I-LOC'), ('.', 'O'), ('Its', 'O'), ('headquarters', 'O'), ('are', 'O'), ('in', 'O'), ('D', 'I-LOC'), ('##UM', 'I-LOC'), ('##BO', 'I-LOC'), (',', 'O'), ('therefore', 'O'), ('very', 'O'), ('##c', 'O'), ('##lose', 'O'), ('to', 'O'), ('the', 'O'), ('Manhattan', 'I-LOC'), ('Bridge', 'I-LOC'), ('.', 'O'), ('[SEP]', 'O')]\n"
     ]
    }
   ],
   "source": [
    "# Bit of a hack to get the tokens with the special tokens\n",
    "tokens = tokenizer.tokenize(tokenizer.decode(tokenizer.encode(sequence)))\n",
    "inputs = tokenizer.encode(sequence, return_tensors=\"pt\")\n",
    "\n",
    "outputs = model(inputs)[0]\n",
    "predictions = torch.argmax(outputs, dim=2)\n",
    "\n",
    "print([(token, label_list[prediction]) for token, prediction in zip(tokens, predictions[0].tolist())])"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
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    "这将输出映射到其预测的每个标记的列表。与管道不同的是，这里每个标记都有一个预测，因为我们没有删除“O”类，这意味着在该标记上找不到特定的实体。"
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